Noise suppression methods and apparatus

ABSTRACT

According to some aspects, a method of suppressing noise in an environment of a magnetic resonance imaging system is provided. The method comprising estimating a transfer function based on multiple calibration measurements obtained from the environment by at least one primary coil and at least one auxiliary sensor, respectively, estimating noise present in a magnetic resonance signal received by the at least one primary coil based at least in part on the transfer function, and suppressing noise in the magnetic resonance signal using the noise estimate.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 and is acontinuation application of U.S. patent application Ser. No. 15/721,309,Attorney Docket No. 00354.70001US04, filed Sep. 29, 2017 and titled“Noise Suppression Methods and Apparatus,” which claims priority under35 U.S.C. § 120 and is a continuation application of U.S. patentapplication Ser. No. 15/387,320, Attorney Docket No. 00354.70001US03,filed Dec. 21, 2016 and titled “Noise Suppression Methods andApparatus,” which claims priority under 35 U.S.C. § 120 and is acontinuation application of U.S. patent application Ser. No. 14/845,949,Attorney Docket No. 00354.70001US01, filed Sep. 4, 2015 and titled“Noise Suppression Methods and Apparatus,” which claims priority under35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No.62/046,814, Attorney Docket No. 00354.70000US00, filed Sep. 5, 2014 andentitled “Low Field Magnetic Resonance Imaging Methods and Apparatus,”U.S. Provisional Patent Application Ser. No. 62/111,320, Attorney DocketNo. 00354.70004US00, filed Feb. 3, 2015 and entitled “Thermal ManagementMethods and Apparatus,” U.S. Provisional Patent Application Ser. No.62/110,049, Attorney Docket No. 00354.70001US00, filed Jan. 30, 2015 andentitled “Noise Suppression Methods and Apparatus,” and U.S. ProvisionalPatent Application Ser. No. 62/174,666, Attorney Docket No.00354.70009US00, filed Jun. 12, 2015 and entitled “AutomaticConfiguration of a Low Field Magnetic Resonance Imaging System,” each ofthe above applications of which is herein incorporated by reference inits entirety.

BACKGROUND

Magnetic resonance imaging (MRI) provides an important imaging modalityfor numerous applications and is widely utilized in clinical andresearch settings to produce images of the inside of the human body. Asa generality, MRI is based on detecting magnetic resonance (MR) signals,which are electromagnetic waves emitted by atoms in response to statechanges resulting from applied electromagnetic fields. For example,nuclear magnetic resonance (NMR) techniques involve detecting MR signalsemitted from the nuclei of excited atoms upon the re-alignment orrelaxation of the nuclear spin of atoms in an object being imaged (e.g.,atoms in the tissue of the human body). Detected MR signals may beprocessed to produce images, which in the context of medicalapplications, allows for the investigation of internal structures and/orbiological processes within the body for diagnostic, therapeutic and/orresearch purposes.

MRI provides an attractive imaging modality for biological imaging dueto the ability to produce non-invasive images having relatively highresolution and contrast without the safety concerns of other modalities(e.g., without needing to expose the subject to ionizing radiation,e.g., x-rays, or introducing radioactive material to the body).Additionally, MRI is particularly well suited to provide soft tissuecontrast, which can be exploited to image subject matter that otherimaging modalities are incapable of satisfactorily imaging. Moreover, MRtechniques are capable of capturing information about structures and/orbiological processes that other modalities are incapable of acquiring.However, there are a number of drawbacks to MRI that, for a givenimaging application, may involve the relatively high cost of theequipment, limited availability (e.g., difficulty in gaining access toclinical MRI scanners) and/or the length of the image acquisitionprocess.

The trend in clinical MRI has been to increase the field strength of MRIscanners to improve one or more of scan time, image resolution, andimage contrast, which, in turn, continues to drive up costs. The vastmajority of installed MRI scanners operate at 1.5 or 3 tesla (T), whichrefers to the field strength of the main magnetic field B₀. A rough costestimate for a clinical MRI scanner is approximately one million dollarsper tesla, which does not factor in the substantial operation, service,and maintenance costs involved in operating such MRI scanners.

These high-field MRI systems typically require large superconductingmagnets and associated electronics to generate a strong uniform staticmagnetic field (B₀) in which an object (e.g., a patient) is imaged. Thesize of such systems is considerable with a typical high-field MRIinstallment including multiple rooms for the magnet, electronics,thermal management system, and control console areas. The size andexpense of high-field MRI systems generally limits their usage tofacilities, such as hospitals and academic research centers, which havesufficient space and resources to purchase and maintain them. The highcost and substantial space requirements of high-field MRI systemsresults in limited availability of MRI scanners. As such, there arefrequently clinical situations in which an MRI scan would be beneficial,but due to one or more of the limitations discussed above, is notpractical or is impossible, as discussed in further detail below.

SUMMARY

The inventors have developed noise suppression and/or avoidancetechniques that are based on noise measurements obtained from theenvironment. The noise measurements are subsequently used to reduce thenoise present in MR signals detected by a magnetic resonance imaging(MRI) system during operation, either by suppressing the environmentalnoise, configuring the MRI system to operate in a frequency band or binhaving less noise, or both.

Some embodiments include a method of suppressing noise in an environmentof a magnetic resonance imaging system, the method comprising estimatinga transfer function based on multiple calibration measurements obtainedfrom the environment by at least one primary coil and at least oneauxiliary sensor, respectively, estimating noise present in a magneticresonance signal received by the at least one primary coil based atleast in part on the transfer function, and suppressing noise in themagnetic resonance signal using the noise estimate.

Some embodiments include a magnetic resonance imaging system comprisingat least one primary coil, at least one auxiliary sensor, at least onecontroller configured to cause the at least one primary coil and the atleast one auxiliary sensor to each obtain multiple calibrationmeasurements from an environment of the magnetic resonance imagingsystem, and to estimate a transfer function based on the respectivemultiple calibration measurements, the controller further configured toestimate noise present in a magnetic resonance signal received by the atleast one primary coil based at least in part on the transfer function,and to suppress noise in the magnetic resonance signal using the noiseestimate.

Some embodiments include a method of operating a magnetic resonanceimaging (MRI) system to avoid noise in an environment of the MRI system,the method comprising obtaining at least one noise signal from theenvironment present within each of a plurality of frequency bins withina spectrum of interest, selecting one of the plurality of frequency binsbased, at least in part, on the respective at least one noise signal,and configuring at least one primary transmit/receive coil of thelow-field MRI system to operate at a frequency within the selectedfrequency bin.

Some embodiments include a magnetic resonance imaging (MRI) systemcapable of being configured to operate in different modes to avoid noisein an environment of the MRI system, the MRI system comprising at leastone primary transmit/receive coil to detect magnetic resonance signals,and at least one controller configured to obtain at least one noisesignal from the environment present within each of a plurality offrequency bins within a spectrum of interest, select one of theplurality of frequency bins based, at least in part, on the respectiveat least one noise signal, and configure the at least one primarytransmit/receive coil to operate at a frequency within the selectedfrequency bin.

Some embodiments include a method of suppressing noise detected in anenvironment of a magnetic resonance imaging system, the methodcomprising acquiring at least one first magnetic resonance signal byapplying a first pulse sequence using a first spatial encoding,acquiring at least one second magnetic resonance signal by applying thefirst pulse sequence using the first spatial encoding, computing adifference between the at least one first magnetic resonance signal andthe at least one second magnetic resonance signal, and estimating noisebased, at least in part, on the computed difference.

Some embodiments include an apparatus for suppressing noise detected inan environment of a magnetic resonance imaging system, the systemcomprising at least one receive coil configured to detect magneticresonance signals, at least one gradient coil for spatial encoding, andat least one controller configured to operate the at least one receivecoil and the at least one gradient coil according to a first pulsesequence using a first spatial encoding to acquire at least one firstmagnetic resonance signal, operate the at least one receive coil and theat least one gradient coil according to the first pulse sequence usingthe first spatial encoding to acquire at least one second magneticresonance signal, compute a difference between the at least one firstmagnetic resonance signal and the at least one second magnetic resonancesignal, and estimate noise based, at least in part, on the computeddifference.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and embodiments of the disclosed technology will bedescribed with reference to the following figures. It should beappreciated that the figures are not necessarily drawn to scale.

FIG. 1 illustrates a block diagram of illustrative components of amagnetic resonance imaging (MRI) system.

FIG. 2 illustrates exemplary components of an MRI system used forperforming noise suppression, in accordance with some embodiments of thetechnology described herein.

FIG. 3 illustrates exemplary components of an MRI system used forperforming noise suppression, in accordance with some embodiments of thetechnology described herein.

FIG. 4 illustrates exemplary components of an MRI system used forperforming noise suppression, in accordance with some embodiments of thetechnology described herein.

FIG. 5 illustrates exemplary components of an MRI system used forperforming noise suppression, in accordance with some embodiments of thetechnology described herein.

FIG. 6 is a flowchart of an illustrative process for performing noisesuppression, in accordance with some embodiments of the technologydescribed herein.

DETAILED DESCRIPTION

The MRI scanner market is overwhelmingly dominated by high-fieldsystems, and is exclusively so for medical or clinical MRI applications.As discussed above, the general trend in medical imaging has been toproduce MRI scanners with increasingly greater field strengths, with thevast majority of clinical MRI scanners operating at 1.5 T or 3 T, withhigher field strengths of 7 T and 9 T used in research settings. As usedherein, “high-field” refers generally to MRI systems presently in use ina clinical setting and, more particularly, to MRI systems operating witha main magnetic field (i.e., a B0 field) at or above 1.5 T, thoughclinical systems operating between 0.5 T and 1.5 T are typically alsoconsidered “high-field.” By contrast, “low-field” refers generally toMRI systems operating with a B0 field of less than or equal toapproximately 0.2 T.

The appeal of high-field MRI systems include improved resolution and/orreduced scan times compared to lower field systems, motivating the pushfor higher and higher field strengths for clinical and medical MRIapplications. However, as discussed above, increasing the field strengthof MRI systems yields increasingly more expensive and complex MRIscanners, thus limiting availability and preventing their use as ageneral purpose and/or generally available imaging solution.

Low-field MRI has been explored in limited contexts for non-imagingresearch purposes and narrow and specific contrast-enhanced imagingapplications, but is conventionally regarded as being unsuitable forproducing clinically useful images. For example, the resolution,contrast, and/or image acquisition time is generally not regarded asbeing suitable for clinical purposes such as, but not limited to, tissuedifferentiation, blood flow or perfusion imaging, diffusion-weighted(DW) or diffusion tensor (DT) imaging, functional MRI (fMRI), etc.

The inventors have developed techniques for producing improved quality,portable and/or lower-cost low-field MRI systems that can improve thewide-scale deployability of MRI technology in a variety of environmentsbeyond the large MRI installments at hospitals and research facilities.As such, low-field MRI presents an attractive imaging solution,providing a relatively low cost, high availability alternative tohigh-field MRI. In particular, low-field MRI systems can be implementedas self-contained systems that are deployable in a wide variety ofclinical settings where high-field MRI systems cannot, for example, byvirtue of being transportable, cartable or otherwise generally mobile soas to be deployable where needed. As a result, such low-field MRIsystems may be expected to operate in generally unshielded or partiallyshielded environments (e.g., outside of specially shielded rooms orencompassing cages) and handle the particular noise environment in whichthey are deployed.

Some aspects of the inventors' contribution derive from theirrecognition that performance of a flexible low-field MRI systems (e.g.,a generally mobile, transportable or cartable system and/or a systemthat can be installed in a variety of settings such as in an emergencyroom, office or clinic) may be particularly vulnerable to noise, such asRF interference, to which many conventional high field MRI systems arelargely immune due to being installed in specialized rooms withextensive shielding. In particular, such systems may be required tooperate in unshielded or partially shielded environments, as well as inmultiple environments that may have different and/or variable sources ofnoise to contend with.

To facilitate low field MRI systems that can be flexibly and widelydeployed, the inventors have developed noise suppression techniques foruse with low-field MRI systems in order to eliminate or mitigateunwanted noise or to reduce its impact on the operation of the low-fieldsystems. According to some embodiments, noise suppression and/oravoidance techniques are based on noise measurements obtained from theenvironment. The noise measurements are subsequently used to reduce thenoise present in MR signals detected by the low-field MRI system (e.g.,a system having a B₀ field of approximately 0.2 T or less, approximately0.1 T or less, approximately 50 mT or less, approximately 20 mT or less,approximately 10 mT or less, etc.) during operation, either bysuppressing the environmental noise, configuring the low-field MRIsystem to operate in a frequency band or bin having less noise, or both.Thus, the low-field MRI system compensates for noise present in whateverenvironment the system is deployed and can therefore operate inunshielded or partially shielded environments and are not limited tospecialized shielded rooms.

Noise suppression techniques developed by the inventors are described inmore detail below and it should be appreciated that the noisesuppression techniques described herein may be used with any suitablelow-field or high-field MRI systems deployed in virtually any facility,including portable and cartable MRI systems. Non-limiting examples oflow-field MRI systems for which the noise suppression techniquesdescribed herein may be used are described in co-filed U.S. patentapplication under Attorney Docket No.: O0354.70000US01, filed Sep. 4,2015 and titled “Low Field Magnetic Resonance Imaging Methods andApparatus,” and/or described in co-filed U.S. patent application underAttorney Docket No.: 00354.70004US01, filed Sep. 4, 2015 and titled“Thermal Management Methods and Apparatus,” each of which is hereinincorporated by reference in its entirety. While aspects of noisesuppression described herein may be particularly beneficial in thelow-field context where extensive shielding may be unavailable orotherwise not provided, it should be appreciated that the techniquesdescribed herein are also suitable in the high-field context and are notlimited for use with any particular type of MRI system.

Accordingly, aspects of the technology described herein relate toimproving the performance of low-field MRI systems in environments wherethe presence of noise, such as RF interference, may adversely impact theperformance of such systems. In some embodiments, a low-field MRI systemmay be configured to detect noise (e.g., environmental noise, internalsystem noise, radio frequency interference, etc.) and, in response,adapt the low-field MRI system to reduce the impact of the noise on theoperation of the system. The low-field MRI system may be configured toreduce the impact of the noise by suppressing noise in the RF signalobtained by the RF receive coil, by generating RF signals thatdestructively interfere with noise in the environment (e.g., RFinterference), by adjusting characteristics of the magnetic fieldsproduced (e.g., adjusting the magnetic field strength of the B0 magnet)and/or received by the low-field MRI system so that the transmit/receivecoils operate in a frequency band satisfactorily free from interference,or using a combination of these techniques.

According to some embodiments, noise suppression techniques describedherein allow a MRI system to be operated in unshielded or partiallyshielded environments, at least in part by adapting noise compensationto the particular environment in which the MRI system is deployed. As aresult, deployment of an MRI system is not confined to speciallyshielded rooms or other customized facilities and instead can beoperated in a wide variety of environments.

In some embodiments, a system may be configured to obtain informationabout noise in the system's environment or within the system itself(e.g., RF interference) and suppress noise in the RF signal measured bythe RF receive coil based, at least in part, on the obtainedinformation. The system may be configured to obtain information aboutnoise in the environment by using one or more auxiliary sensors. Theterm “auxiliary” is used to differentiate between a sensor or detectorcapable of detecting noise and the primary receive channel that receivesMR signals for use in MRI. It should be appreciated that, in someembodiments, an auxiliary sensor may also receive one or more MRsignals. For example, the low-field MRI system may comprise one or moreauxiliary RF receive coils positioned proximate to the primarytransmit/receive coil(s), but outside of the field of view of the B0field, to detect RF noise without detecting MR signals emitted by asubject being imaged. The noise detected by the auxiliary RF coil(s) maybe used to suppress the noise in the MR signal obtained by the primaryRF coil of the MRI system.

Such an arrangement has the ability to dynamically detect and suppressRF noise to facilitate the provision of, for example, a generallytransportable and/or cartable low-field MRI system that is likely to besubjected to different and/or varying levels of RF noise depending onthe environment in which the low-field MRI system is operated. That is,because noise suppression is based on the current noise environment,techniques described herein provide noise suppression capabilityspecific to the particular environment in which the system is deployed.

The inventors have recognized that the simplistic approach ofsubtracting samples of noise obtained by one or more auxiliary sensorsfrom the signal measured by the primary receive coil(s) may provideunsatisfactory noise suppression, even if the gain of the noise detectedby the auxiliary sensor(s) is adjusted. The primary receive coil(s) andthe auxiliary sensor(s) may measure different noise signals because theprimary coil(s) and the auxiliary sensor(s) may be in differentlocations, have different orientations, and/or may have differentphysical characteristics (e.g., may have a different number of coilturns, may differ in size, shape, impedance, or may be a different typeof sensor altogether).

Different locations and/or orientations of the primary coil(s) and theauxiliary sensor(s) may lead to differences in the characteristics ofthe noise signals received by the primary coil and the auxiliary sensor.Different physical characteristics between the primary coil(s) andauxiliary sensor(s) may lead to frequency-dependent differences betweennoise signals received by the primary coil(s) and auxiliary sensor(s).As a result, subtracting the noise signal measured by one or moreauxiliary sensors from the signal measured by the primary coil(s) maynot adequately suppress noise detected by the primary coil(s). Even ifthe noise signal measured by the auxiliary sensor(s) were scaled by aconstant in an attempt to compensate for differences in the gain of thenoise signals received by the primary coil(s) and auxiliary sensor(s),such compensation would not account for frequency-dependent differencesin the noise signals.

Accordingly, in some embodiments, a transfer function is estimated andused to suppress noise in the RF signal received by one or more primaryreceive coil(s) of a low-field MRI system. As discussed in furtherdetail below, the transfer function may operate to transform a noisesignal received via one or multiple auxiliary sensors (e.g., one or moreauxiliary RF coils and/or other types of sensors described herein) to anestimate of the noise received by the primary receive coil (or multipleprimary receive coils). In some embodiments, noise suppression maycomprise: (1) obtaining samples of noise by using the one or moreauxiliary sensor(s); (2) obtaining samples of the MR data using theprimary RF coil; (3) obtaining a transfer function; (4) transforming thenoise samples using the transfer function; and (5) subtracting thetransformed noise samples from the obtained MR data to suppress and/oreliminate noise.

The transfer function may be estimated from multiple (e.g., at leastten, at least 100, at least 1000, etc.) calibration measurementsobtained using the auxiliary sensor(s) and primary coil(s). Multiplecalibration measurements allow for estimating the transfer function withhigh accuracy and, in particular, may allow for estimating the amplitudeand phase of the transfer function for a plurality of frequency binsacross the frequency spectrum for which the transfer function isdefined. For example, when processing signals using an K-point DFT(e.g., where K is an integer equal to 128, 256, 512, 1024 etc.),multiple measurements may allow for estimating the amplitude and phaseof the transfer function for each of the K frequency bins.

In some embodiments, multiple auxiliary receive coils may be used asauxiliary sensors to suppress noise received by the primarytransmit/receive coil(s) of a low-field MRI system. For example, in someembodiments, a low-field MRI system may include multiple RF coilspositioned/configured to sense the MR signal emitted by the subjectbeing imaged (e.g., multiple “primary” coils) and/or multiple coilspositioned/configured to receive noise data, but to detect little or noMR signal (e.g., multiple “auxiliary” coils). Such an arrangementfacilitates detection and characterization of multiple noise sources tosuppress a variety of noise that may be present in a given environment.Multiple primary receive coils may also be used that factor into thenoise characterization techniques described herein, as well as beingused to accelerate image acquisition via parallel MR, or in othersuitable ways, as discussed in further detail below.

In some embodiments, multiple auxiliary sensors may be used to performnoise compensation when there are multiple sources of noise in theenvironment of the low-field MRI system. For example, one or moreauxiliary RF coils and/or one or more other types of sensors may be usedto obtain information about the noise environment resulting from noiseproduced by multiple sources, which information in turn may be used toprocess the RF signal received by the primary receive coil(s) in orderto compensate for the noise produced by multiple sources. For example,in some embodiments, a multichannel transfer function may be estimatedfrom calibration measurements obtained using multiple auxiliary sensorsand the primary RF coil(s), as described in more detail below. Themultichannel transfer function may represent the relationships among thenoise signals captured by the primary RF coil(s) and each of themultiple auxiliary sensors. For example, the transfer function maycapture correlation among the noise signals received by the multipleauxiliary sensors. The transfer function may also capture correlationamong the noise signals receive by the multiple auxiliary sensors andthe noise signal received by the primary RF coil(s).

In some embodiments, multiple auxiliary sensors may be used to performnoise suppression by: (1) obtaining samples of noise by using multipleauxiliary sensors; (2) obtaining samples of the MR data using theprimary RF coil(s); (3) obtaining a multichannel transfer function; (4)transforming the noise samples using the multichannel transfer function;and (5) subtracting the transformed noise samples from the obtained MRdata to suppress and/or eliminate noise.

In some embodiments, the multichannel transfer function may be estimatedfrom multiple (e.g., at least ten, at least 100, at least 1000, etc.)calibration measurements. The multiple calibration measurements allowfor estimating the multichannel transfer function with high accuracyand, in particular, may allow for estimating the amplitude and phase ofthe transfer function for a plurality of frequency bins across which themultichannel transfer function is defined. For example, when processingsignals using a K-point DFT (e.g., where K is an integer equal to 128,256, 512, 1024 etc.), multiple calibration measurements may allow forestimating the amplitude and phase of the multichannel transfer functionfor each of the K frequency bins.

The inventors have further appreciated that the MR signal detected byone or more primary receive coils may also be utilized to characterizethe noise to suppress or eliminate noise from the MR data. Inparticular, the inventors have recognized that by repeating MR dataacquisitions using the same spatial encoding (e.g., by repeating a pulsesequence with the same operating parameters for the gradient coils), the“redundant” data acquired can be used to characterize the noise. Forexample, if a pulse sequence is repeated with the same spatial encodingmultiple times, the MR data obtained should in theory be the same. Thus,the difference in the signals acquired from multiple acquisitions usingthe same spatial encoding can be presumed to have resulted from noise.Accordingly, multiple signals obtained from using the same spatialencoding may be phase shifted and subtracted (or added) to obtain ameasure of the noise.

According to some embodiments, noise characterized in this manner can beused to compute a transfer function or included as a channel in amulti-channel transfer function, as discussed in further detail below.Alternatively, noise characterized in this manner can be used alone orin combination with other techniques to suppress noise from acquired MRsignals. For example, a noise estimate obtained based on multiple MRsignals obtained using the same spatial encoding may be used to suppressnoise without computing a transfer function, as other suitabletechniques may be used.

The inventors have further appreciated that one or more sensors (e.g.,one or more RF coils or other sensors capable of detectingelectromagnetic fields) may be used to assess the noise background in aspectrum of interest to assess which band within the spectrum iscleanest from a noise perspective so that the transmit/receive coil(s)may be configured to operate in the identified frequency band.Accordingly, in some embodiments, a low-field MRI system may be adaptedby adjusting the transmit/receive coil(s) to operate at a frequency bandhaving less interference relative to other frequency bands in which thetransmit/receive coil(s) can be configured to operate. For example, oneor more auxiliary RF coils may be configured to monitor noise acrossmultiple frequency bands over which the primary RF coil could operateand, the primary RF coil may be configured to operate at the frequencyband having the least amount of noise, as determined by the measurementsobtained using the auxiliary RF coils. In particular, an auxiliary RFcoil may be a wideband RF coil configured to measure the noise level(e.g., noise floor) across a wide band of frequencies. Based on thenoise measured across a frequency band of interest, the primarytransmit/receive coil(s) (e.g., which may be a narrowband coil) may beconfigured to operate in a band determined to have less noise than otherfrequency bands. Alternatively, multiple sensors may be provided, eachmeasuring noise levels in a respective frequency band. The primarytransmit/receive coil(s) may then be configured to operate in thefrequency band determined to have the least amount of noise present.

The inventors have also appreciated that a significant source ofinterference for a low-field MRI system may be one or more power lines(e.g., power cords) supplying power to the low-field MRI system.Accordingly, in some embodiments, a low-field MRI system is configuredto measure directly any interference due to the power line(s) and usethe measurements to suppress or cancel such interference. For example,in some embodiments, a low-field MRI system may include one or moresensors coupled to a power line of the system to measure any RF signalsproduced or carried by the power line, and the measurements obtained bythe sensor(s) may be used as part of the noise suppression techniquesdescribed herein (e.g., to further characterize the noise environmentand facilitate estimation of a comprehensive transfer function).

In some embodiments, a low-field MRI system may include an antennacapacitively coupled to one of the power lines of the system and may beconfigured to use measurements obtained by the antenna to suppress noisein the RF signal received by the primary RF coil of the low-field MRIsystem. Such an antenna may be of any suitable type and, for example,may comprise a thin metal sheet wrapped around the power line and/or oneor more capacitors coupled to the power line. A low-field MRI system mayinclude multiple such antenna to detect noise resulting from any desirednumber of power lines supplying power to the system (or that otherwiseimpact the system) including, for example, hot lines carryingsingle-phase, two-phase, or three-phase power. In some instances, alow-field MRI system may include such an antenna for a ground wire. Asanother example, a low-field MRI system may include a sensor inductivelycoupled to a power line or multiple respective power lines (e.g., by useof a toroid or any other suitable method) to measure RF signals carriedby the power line such that these measurements may be used to suppressnoise in the RF signal measured by the primary RF coil of the low-fieldMRI system.

In some embodiments, a sensor's measurements of interference due to apower line may be used to suppress noise in the RF signal measured bythe primary RF receive coil by estimating a transfer function betweenthe primary RF receive coil and the sensor. This may be done in anysuitable way and, for example, may be done using the techniquesdescribed herein for estimating a transfer function between the primaryRF receive coil and an auxiliary RF receive coil. For example, noisecharacterized in this manner may be used to estimate a transfer functionalone or may be a channel in a multi-channel transfer function. Noisecharacterized by a sensor coupled to one or more power lines may beutilized in other manners (e.g., used directly to suppress noise), asthe aspects are not limited in this respect.

The inventors have further appreciated that noise in the environment maybe detected by coupling one or more sensors to one or moreelectromagnetic interference (EMI) shields. For example, a sensor may beconnected inductively or capacitively between one or more EMI shieldsand ground to detect the EMI captured by the shield. Noise characterizedin this manner may be used to suppress or eliminate noise from MRsignals detected by the primary receive coil(s). For example, noisecharacterized by coupling a sensor to one or more EMI shields may beused to estimate a transfer function alone, or may be used as a channelin a multi-channel transfer function. Noise characterized by a sensorcoupled to one or more EMI shields may be utilized in other manners, asthe aspects are not limited in this respect.

According to some embodiments, noise from various sources arecharacterized using a combination of the above described techniques todetermine a multi-channel transfer function that can be used to suppressor eliminate noise from the various noise sources. Noise measurementsmay be obtained during operation of the MRI system so that amulti-channel transfer function may be determined dynamically, allowingfor noise suppression that adapts to the changing noise environment ofthe MRI system. However, noise in the environment may be characterizedupon system start-up, when the system is moved to a different locationand/or upon the occurrence of any event, and the characterized noiseused to suppress and/or eliminate noise in acquired MR signals, as thetechniques described herein can be applied as desired.

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, methods and apparatus for noisesuppression and/or cancellation. It should be appreciated that variousaspects described herein may be implemented in any of numerous ways.Examples of specific implementations are provided herein forillustrative purposes only. In addition, the various aspects describedin the embodiments below may be used alone or in any combination, andare not limited to the combinations explicitly described herein.

FIG. 1 is a block diagram of exemplary components of a MRI system 100.While the noise suppression techniques may have particular benefits fora low-field MRI system, the techniques described herein are not limitedfor use at low-field and may be employed to suppress noise in thehigh-field context, as the aspects are not limited in this respect. Inthe illustrative example of FIG. 1, MRI system 100 comprises workstation104, controller 106, pulse sequences store 108, power management system110, and magnetic components 120. It should be appreciated that system100 is illustrative and that a MRI system may have one or more othercomponents of any suitable type in addition to or instead of thecomponents illustrated in FIG. 1.

As illustrated in FIG. 1, magnetic components 120 comprises B₀ magnet122, shim coils 124, RF transmit and receive coils 126, and gradientcoils 128. B₀ magnet 122 may be used to generate, at least in part, themain magnetic field B₀. B₀ magnet 122 may be any suitable type of magnetthat can generate a main magnetic field (e.g., a low-field strength ofapproximately 0.2 T or less), and may include one or more B₀ coils,correction coils, etc. Shim coils 124 may be used to contribute magneticfield(s) to improve the homogeneity of the B₀ field generated by magnet122. Gradient coils 128 may be arranged to provide gradient fields and,for example, may be arranged to generate gradients in the magnetic fieldin three substantially orthogonal directions (X, Y, Z) to localize whereMR signals are induced.

RF transmit and receive coils 126 may comprise one or more transmitcoils that may be used to generate RF pulses to induce a magnetic fieldB₁. The transmit coil(s) may be configured to generate any suitable typeof RF pulses configured to excite an MR response in a subject and detectthe resulting MR signals emitted. RF transmit and receive coils 126 mayinclude one or multiple transmit coils and one or multiple receivecoils. The transmit and receive coils may be implemented using the samecoils or may be implemented using separate coils for transmit andreceive, and are referred to generally as transmit/receive coils orTx/Rx coils. Each of magnetics components 120 may be constructed in anysuitable way. For example, in some embodiments, one or more of magneticscomponents 120 may be fabricated using the laminate techniques describedin the above incorporated co-filed applications.

Power management system 110 includes electronics to provide operatingpower to one or more components of the low-field MRI system 100. Forexample, as discussed in more detail below, power management system 110may include one or more power supplies, gradient power amplifiers,transmit coil amplifiers, and/or any other suitable power electronicsneeded to provide suitable operating power to energize and operatecomponents of the low-field MRI system 100.

As illustrated in FIG. 1, power management system 110 comprises powersupply 112, amplifier(s) 114, transmit/receive switch 116, and thermalmanagement components 118. Power supply 112 includes electronics toprovide operating power to magnetic components 120 of the low-field MRIsystem 100. For example, power supply 112 may include electronics toprovide operating power to one or more B₀ coils (e.g., B₀ magnet 122) toproduce the main magnetic field for the low-field MRI system. In someembodiments, power supply 112 is a unipolar, continuous wave (CW) powersupply, however, any suitable power supply may be used. Transmit/receiveswitch 116 may be used to select whether RF transmit coils or RF receivecoils are being operated.

Amplifier(s) 114 may include one or more RF receive (Rx) pre-amplifiersthat amplify MR signals detected by one or more RF receive coils (e.g.,coils 124), one or more RF transmit (Tx) amplifiers configured toprovide power to one or more RF transmit coils (e.g., coils 126), one ormore gradient power amplifiers configured to provide power to one ormore gradient coils (e.g., gradient coils 128), shim amplifiersconfigured to provide power to one or more shim coils (e.g., shim coils124).

Thermal management components 118 provide cooling for components oflow-field MRI system 100 and may be configured to do so by facilitatingthe transfer of thermal energy generated by one or more components ofthe low-field MRI system 100 away from those components. Thermalmanagement components 118 may include, without limitation, components toperform water-based or air-based cooling, which may be integrated withor arranged in close proximity to MRI components that generate heatincluding, but not limited to, B₀ coils, gradient coils, shim coils,and/or transmit/receive coils. Thermal management components 118 mayinclude any suitable heat transfer medium including, but not limited to,air and water, to transfer heat away from components of the low-fieldMRI system 100.

As illustrated in FIG. 1, low-field MRI system 100 includes controller106 (also referred to as a console) having control electronics to sendinstructions to and receive information from power management system110. Controller 106 may be configured to implement one or more pulsesequences, which are used to determine the instructions sent to powermanagement system 110 to operate the magnetic components 120 in adesired sequence. For example, controller 106 may be configured tocontrol power management system 110 to operate the magnetic components120 in accordance with a balance steady-state free precession (bSSFP)pulse sequence, a low-field gradient echo pulse sequence, a low-fieldspin echo pulse sequence, a low-field inversion recovery pulse sequence,and/or any other suitable pulse sequence. Controller 106 may beimplemented as hardware, software, or any suitable combination ofhardware and software, as aspects of the disclosure provided herein arenot limited in this respect.

In some embodiments, controller 106 may be configured to implement apulse sequence by obtaining information about the pulse sequence frompulse sequences repository 108, which stores information for each of oneor more pulse sequences. Information stored by pulse sequencesrepository 108 for a particular pulse sequence may be any suitableinformation that allows controller 106 to implement the particular pulsesequence. For example, information stored in pulse sequences repository108 for a pulse sequence may include one or more parameters foroperating magnetics components 120 in accordance with the pulse sequence(e.g., parameters for operating the RF transmit and receive coils 126,parameters for operating gradient coils 128, etc.), one or moreparameters for operating power management system 110 in accordance withthe pulse sequence, one or more programs comprising instructions that,when executed by controller 106, cause controller 106 to control system100 to operate in accordance with the pulse sequence, and/or any othersuitable information. Information stored in pulse sequences repository108 may be stored on one or more non-transitory storage media.

As illustrated in FIG. 1, controller 106 also interacts with computingdevice 104 programmed to process received MR data. For example,computing device 104 may process received MR data to generate one ormore MR images using any suitable image reconstruction process(es).Controller 106 may provide information about one or more pulse sequencesto computing device 104 for the processing of data by the computingdevice. For example, controller 106 may provide information about one ormore pulse sequences to computing device 104 and the computing devicemay perform an image reconstruction process based, at least in part, onthe provided information.

Computing device 104 may be any electronic device that may processacquired MR data and generate one or more images of the subject beingimaged. In some embodiments, computing device 104 may be a fixedelectronic device such as a desktop computer, a server, a rack-mountedcomputer, or any other suitable fixed electronic device that may beconfigured to process MR data and generate one or more images of thesubject being imaged. Alternatively, computing device 104 may be aportable device such as a smart phone, a personal digital assistant, alaptop computer, a tablet computer, or any other portable device thatmay be configured to process MR data and generate one or images of thesubject being imaged. In some embodiments, computing device 104 maycomprise multiple computing devices of any suitable type, as the aspectsare not limited in this respect. A user 102 may interact withworkstation 104 to control aspects of the low-field MR system 100 (e.g.,program the system 100 to operate in accordance with a particular pulsesequence, adjust one or more parameters of the system 100, etc.) and/orview images obtained by the low-field MR system 100.

FIG. 2 shows illustrative components of a portion of an example a MRIsystem that may be used for performing noise suppression, in accordancewith some embodiments of the technology described herein. For example,transmit/receive system 200 may form at least part of thetransmit/receive equipment (e.g., transmit/receive coils 126, one ormore controllers, etc.) of a low-field MRI system, such as any of theexemplary systems described in the above incorporated co-filed patentapplications. Transmit/receive system 200 is configured to detect MRsignals emitted from excited atoms of a subject 204 being imaged, and tocharacterize noise in the environment to suppress or remove thecharacterized noise from the detected MR signals, as discussed infurther detail below.

As shown in FIG. 2, transmit/receive system 200 comprises a primary RFreceive coil 202 configured to measure MR signals emitted by the subject204 in response to an excitation pulse sequence (e.g., a pulse sequenceselected from pulse sequence repository 108 and executed by controller102). The excitation pulse sequence may be produced by primary RFreceive coil 202 and/or by one or more other transmit RF coils arrangedproximate subject 204 and configured to produce suitable MR pulsesequences when operated. Primary receive coil 202 may be a single coilor may be a plurality of coils, which, in the latter case, may be usedto perform parallel MRI. Tuning circuitry 208 facilitates operation ofprimary receive coil 202 and signals detected by RF coil(s) 202 areprovided to acquisition system 210, which may amplify the detectedsignals, digitize the detected signals, and/or perform any othersuitable type of processing.

Transmit/receive system 200 also includes auxiliary sensor(s) 206, whichmay include any number or type of sensor(s) configured to detect orotherwise measure noise sources in the environment and/or environmentalnoise produced by the MRI system itself. The noise measured by auxiliarysensor(s) 206 may be characterized and used to suppress noise in the MRsignal detected by primary RF coil(s) 202 using techniques described infurther detail below. After acquisition system 210 processes the signalsdetected by RF coil(s) 202 and auxiliary sensor(s) 206, acquisitionsystem 210 may provide the processed signals to one or more othercomponents of the MRI system for further processing (e.g., for use informing one or more MR images of subject 204). Acquisition system 210may comprise any suitable circuitry and may comprise, for example, oneor more controllers and/or processors configured to control the MRIsystem to perform noise suppression in accordance with embodimentsdescribed herein. It should be appreciated that components illustratedin FIG. 2 may be configured to detect MR signals generated by a MRIsystem and, for example, the RF coils may be similar or the same asthose described in the above incorporated co-field applications, or maybe any other suitable type of coil.

In some embodiments, auxiliary sensor(s) 206 may include one or moreauxiliary coils 306 configure to measure noise from one or more noisesources in the environment in which the MRI system is operating, asshown in FIG. 3. In some instances, the auxiliary RF coil(s) 306 may beconstructed to be substantially more sensitive to ambient noise than toany noise generated by the coil itself. For example, the auxiliary RFcoil 306 may have a sufficiently large aperture and/or a number of turnssuch that the auxiliary coil is more sensitive to noise from theenvironment than to noise generated by the auxiliary coil itself. Insome embodiments, auxiliary RF coil(s) 306 may have a larger apertureand/or a greater number of turns than primary RF coil(s) 202. However,auxiliary RF coil(s) 306 may be the same as primary RF coil in thisrespect and/or may differ from primary RF coil(s) 202 in other respects,as the techniques described herein are not limited to any particularchoice of coils. For example, in some embodiments, an auxiliary sensorof a different type is used in place of an RF coil type sensor, asdiscussed in further detail below.

In the illustrative embodiment of FIG. 3, auxiliary RF coil(s) 306is/are located a distance 305 apart from primary RF coil 202. Thedistance 305 may be selected such that auxiliary coil(s) 306 is/aresufficiently far away from the sample 204 to avoid sensing MR signalsemitted by the sample during imaging, but otherwise arranged as close aspossible to the primary RF coil 202 so that auxiliary coil(s) 306 detectnoise similar to the noise detected by primary coil(s) 202. In thismanner, the noise from one or more noise sources measured by auxiliarycoil(s) 306 and characterized using techniques discussed herein (e.g.,by using the detected noise to calculate, at least in part, a transferfunction that can be used to suppress and/or eliminate noise present ondetected MR signals) may be representative of the noise detected byprimary coil(s) 202. It should be appreciated that auxiliary coil(s) 306need not be RF coils, but may be any type of sensor capable of detectingor measuring noise in the environment that may impact the performance ofthe MRI system, as the techniques described herein are not limited foruse with any particular type of sensor.

According to some embodiments, auxiliary sensor(s) 206 may include oneor more auxiliary sensors 406 configure to measure noise by couplingsensor(s) to one or more components of the MRI system, as schematicallyshown in FIG. 4. For example, auxiliary sensors 406 may include one ormore sensors coupled to one or more components of the MRI system orotherwise arranged to detect noise produced by the MRI system. Asdiscussed above, power cables are frequently a source of noise that canhave a negative impact on the operation of the MRI system and, inparticular, may produce noise that is detected by the one or moreprimary coils. According to some embodiments, auxiliary sensor(s) 406include one or more sensors coupled (e.g., capacitively or inductively)to one or more power cables of the system to detect noise producedtherefrom. The detected noise may be characterized and used to suppressnoise from detected MR signals, for example, by using the detected noiseto produce, at least in part, a transfer function that characterizesnoise detected by the auxiliary sensor(s) 406, or by being directlyapplied to detected MR signals.

As discussed above, the low-field regime may facilitate systems that canbe utilized in a wide variety of circumstances and/or that can begenerally transported from one location to another. As a result,low-field MRI systems will frequently operate outside of speciallyshielded rooms. Thus, some low-field MRI systems may utilize partialshielding of one or more components of the system to prevent at leastsome EMI from reaching the shielded components. The inventors haveappreciated that by coupling one or more sensors to one or more EMIshields (e.g., a Faraday cage of one or more components or the like) ofthe system, the noise absorbed by the one or more EMI shields can bemeasured, characterized and used to suppress and/or eliminate noise fromdetected MR signals. According to some embodiments, auxiliary sensor(s)406 include one or more sensors coupled between one or more EMI shieldsand ground to measure noise absorbed by the EMI shield that can be usedto facilitate noise suppression. For example, the noise detected fromthe EMI shield may be used to compute, at least in part, a transferfunction that can be utilized in suppressing and/or eliminating noisefrom detected MR signals. It should be appreciated that auxiliarysensor(s) 406 may include any other type of sensor capable of detectingnoise, as the aspects are not limited in this respect.

According to some embodiments, auxiliary sensor(s) 206 include theprimary coil(s) itself as illustrated in FIG. 5, wherein the primary RFcoil(s) are labeled both as primary receive coil 202 and auxiliarysensor 506 for the system, as the primary RF coil(s) may perform bothroles in some circumstances. As discussed above, the inventors haverecognized that certain pulse sequences facilitate using the signalsacquired from the primary coil(s) to also suppress noise thereon. Apulse sequence refers generally to operating transmit coil(s) andgradient coil(s) in a prescribed sequence to induce an MR response. Byrepeating the same pulse sequence using the same spatial encoding,“redundant” MR signals can be obtained and used to estimate noisepresent in the MR signals.

To address the relatively low signal-to-noise ratio (SNR) of low-fieldMRI, pulse sequences have been utilized that repeat MR data acquisitionsusing the same spatial encoding (e.g., by repeating a pulse sequencewith the same operating parameters to drive the gradient coils in thesame manner). The MR signals obtained over multiple acquisitions areaveraged to increase the SNR. For example, a balanced steady-state freeprecession (bSSFP) pulse sequence may be used to rapidly obtain MR dataover multiple acquisitions, which acquisitions are then averagedtogether to increase the SNR. The term “average” is used herein todescribe any type of scheme for combining the signals, includingabsolute average (e.g., mean), weighted average, or any other techniquethat can be used to increase the SNR by combining MR data from multipleacquisitions. Because the bSSFP pulse sequence does not require waitingfor the net magnetization to realign with the B₀ field betweensuccessive MR data acquisitions (e.g., successive acquisitions may beobtained without needing to wait for the transverse magnetization vectorto decrease to 0), multiple acquisitions may be rapidly obtained.However, any pulse sequence can be used to perform multiple acquisitionsat the same location, as the aspects are not limited in this respect.

The inventors have appreciated that the MR data obtained during multipleacquisitions performed using the same spatial encoding may be used tosuppress and/or eliminate noise from the detected MR signal. Asdiscussed above, when multiple acquisitions are performed by repeatingthe pulse sequence with the same spatial encoding, the MR signalsobtained should be the same or nearly the same and the differences canbe attributed to noise. As such, phase shifting the MR signal obtainedover multiple acquisitions and computing the difference between thesignals provides a means for evaluating the noise corrupting the MRdata. The difference may be obtained by phase shifting and either addingor subtracting the phase shifted MR signals depending on the type ofpulse sequence utilized. For example, the bSSFP pulse sequence flips thepolarity of the pulse sequence on subsequent acquisitions so that thedifference may be computed by adding MR signals that have beenappropriately shifted in phase. However, MR signals obtained using otherpulse sequences that do not flip the polarity may be subtracted afterbeing appropriately phase shifted to obtain the difference betweenmultiple MR acquisitions. Because multiple acquisitions (e.g., 10, 20,50, 100, 150 or more) obtained using the same spatial encoding mayalready be performed (and averaged) in the low-field context to achievesufficiently large SNR, using one or more of the acquisitions to computea noise estimate will not substantially increase acquisition times, ifat all.

The computed noise (e.g., the difference between MR signals obtainedover multiple acquisitions with the same spatial encoding can be used tosuppress and/or eliminate the noise in the detected MR signal. Accordingto some embodiments, the noise computed according to the above describedtechnique may be used to, at least in part, determine a transferfunction that can be used to suppress and/or eliminate noise in themanner discussed in further detail below. However, noise computed bydetermining the difference between multiple MR acquisitions can beutilized in other ways to suppress and/or eliminate noise, as theaspects are not limited in this respect. For example, noise computedbased on determining the difference between multiple MR acquisitionsobtained from the same location may be directly applied to detected MRsignals or applied after further processing. It should be appreciatedthat the noise computed by comparing multiple acquisitions obtainedusing the same spatial encoding can be used to dynamically suppressand/or eliminate noise from the detected MR signals. In this way, noisecancellation dynamically adapts to changing noise conditions in theenvironment.

As discussed above, noise detected by one or more auxiliary sensors,some examples of which are described in the foregoing, may be used tocharacterize the noise from one or more noise sources and suppressand/or eliminate noise from detected MR signals. According to someembodiments, the noise detected by one or more auxiliary sensors is usedto determine a transfer function that can be used to transform detectednoise to an approximation of the noise detected by the one or moreprimary receive coils. According to some embodiments, noise detected byone or more auxiliary sensors is applied to detected MR signals tosuppress noise without using a transfer function.

As a non-limiting example, a noise suppression component (e.g.,acquisition system 210 illustrated in FIGS. 2-5) may suppress noise in asignal s_(pri)(t), detected by primary RF coil 202, by using the signals_(aux)(t), detected by auxiliary sensor 206, and a primary-to-auxiliarysensor (PA) transfer function H_(PA)(ω) via the following expression:

s _(comp)(t)=s _(pri)(t)−

⁻¹ {H _(PA)(ω)S _(aux)(ω)},  (1)

where S_(aux)(ω) is the Fourier transform of s_(aux)(t),

⁻¹{ } is the inverse Fourier transform operator, and s_(comp)(t) is thenoise-suppressed signal. It should be appreciated that the noisecompensation calculation of Equation (1) may be implemented in any ofnumerous ways and, for example, may be implemented in the frequencydomain or in the time domain, as the noise suppression techniquesdescribed herein are not limited in this respect. Exemplary techniquesfor estimating a PA transfer function are described in more detailbelow.

FIG. 6 is a flowchart of an illustrative process 600 for performingnoise suppression, in accordance with some embodiments of the technologydescribed herein, including a detailed description of a technique fordetermining an exemplary transfer function, first with respect to atransfer function between an auxiliary sensor and a primary receivecoil, followed by a description of a transfer function between multipleauxiliary sensors and a primary receive coil (multi-channel transferfunction). It should be appreciated that a single or multi-channeltransfer function may be computed for any number of receive coils sothat noise cancellation in this respect can be performed using anynumber and type of auxiliary sensor and any number and type of receivecoil. Process 600 may be performed by components of any suitable MRIsystem and, for example, may be performed by components of MRI system100 described with reference to FIG. 1 and the associated componentsillustrated in FIGS. 2-5.

Process 600 begins at acts 602 and 604, where a MRI system obtains MRdata by using a primary RF coil (e.g., RF coil 202) and obtains noisedata using one or more auxiliary sensors (e.g., one or more RF coils 306and/or one or more other sensors 206, 406, 506, etc.). As discussedabove, any number of auxiliary sensors of any type may be used tocharacterize the noise in the environment of the MRI system. Toillustrate aspects of the noise suppression techniques, the case of aprimary RF coil and an auxiliary sensor is first considered. The primaryRF coil and auxiliary sensor may operate to obtain MR and noise datasubstantially simultaneously such that the noise data acquired by theauxiliary sensor may be used to suppress noise in the MR data acquiredby the primary RF coil.

The signal obtained by the primary RF coil may comprise both noise andan MR signal emitted by the sample being imaged. For example, ifs_(pri)(t) represents the total signal measured by the primary RF coil,then s_(pri)(t) may be expressed as:

s _(pri)(t)=m _(pri)(t)+n _(pri)(t)

where m_(pri)(t) and n_(pri)(t) represent the MR signal and noisecomponents of the total signal measured by the primary RF coil. Assumingthat the auxiliary sensor measures a negligible amount of MR signal (dueto the placement of the auxiliary sensor relative to the primary RF coiland the sample being imaged), the signal measured by the auxiliarysensor contains mostly ambient RF noise. For example, if s_(aux)(t)represents the total signal measured by the auxiliary sensor, thens_(aux)(t) may be expressed according to:

s _(aux)(t)=n _(aux)(t),

where n_(aux)(t) is noise measured by the auxiliary sensor.

As discussed above, the noise components of the signals measured by theprimary RF coil and auxiliary sensor may be different (e.g., n_(pri)(t)may be different from n_(aux)(t)) due to physical differences betweenthe primary coil and auxiliary sensor as well as differences in locationand orientation. However, the inventors have appreciated that arelationship between the noise signals measured by the primary coil andthe auxiliary sensor may be established since both measure noise fromone or more common sources. Such a relationship may be, in someembodiments, represented by a primary to auxiliary transfer functionH_(PA)(ω) as detailed below.

For example, in some embodiments, each of the noise signals n_(pri)(t)and n_(aux)(t) may contain noise from several independent sourcesincluding, but not limited to, noise from one or more sources in theenvironment of the low-field MRI system, noise generated by the primaryRF coil and/or the auxiliary sensor, and noise generated by one or moreother components of the MRI system (e.g., noise generated by tuningcircuitry, acquisition system, power cables, etc.). Thus, for example,the noise signals n_(pri)(t) and n_(aux)(t) may be expressed as:

n _(pri)(t)=c _(pri)(t)+u _(pri)(t), and

n _(aux)(t)=C _(aux)(t)u _(aux)(t)≅c _(aux)(t)

where c_(pri)(t) and c_(aux)(t) represent correlated noise (i.e., thesignals c_(pri)(t) and c_(aux)(t) are correlated) generated by one ormore common noise sources detected by the primary coil and the auxiliarysensor, respectively, and where u_(pri)(t) and u_(aux)(t) representuncorrelated noise detected by the primary coil and auxiliary sensors,respectively (e.g., noise generated by the primary coil and auxiliarysensor themselves). As described above, in some embodiments, theauxiliary sensor may be configured such that it is more sensitive tonoise from the environment than noise generated by the sensor itself.For example, the auxiliary sensor may be an auxiliary RF coil having asufficiently large aperture and/or number of turns. As such, c_(aux)(t)may be substantially larger than u_(aux)(t) so thatn_(aux)(t)≅c_(aux)(t).

Each of the noise signals c_(pri)(t) and c_(aux)(t) can be expressed inrelation to the common noise source(s) through a respective measurementtransfer function. For example, in the Fourier domain, the Fouriertransforms C_(pri)(ω) and C_(aux)(ω) of noise signals c_(pri)(t) andc_(aux)(t) can be expressed as:

C _(pri)(ω)=H _(pri)(ω)C _(s)(ω)

C _(aux)(ω)=H _(aux)(ω)C _(s)(ω)

where C_(s) (ω) is the Fourier transform of a common noise source andH_(pri)(ω) and H_(aux)(ω) respectively represent the channel between thecommon noise source and the primary receive coil and auxiliary sensor.Combining the above equations yields:

C_(pri)(ω) = H_(PA)(ω)C_(aux)(ω), where${{H_{PA}(\omega)} = \frac{H_{pri}(\omega)}{H_{aux}(\omega)}},$

is the primary-to-auxiliary ransfer function.

Returning to the discussion of process 600, after the MR and noisesignals are acquired at acts 602 and 604, process 600 proceeds to act606, where a primary-to-auxiliary (PA) transfer function is obtained. Insome embodiments, the PA transfer function may have been previouslyestimated so that obtaining the PA transfer function at act 606comprises accessing a representation of the PA transfer function (e.g.,a frequency-domain or a time-domain representation of the PA transferfunction). In other embodiments, obtaining the PA transfer function atact 606 may comprise estimating and/or updating the estimate of thetransfer function. Techniques for estimating a PA transfer function aredescribed in more detail below.

Next, at act 608, the noise data obtained at act 604 and the PA transferfunction obtained at act 606 may be used to suppress or cancel noise inthe MR data obtained at act 602. This may be done using Equation (1)described above, using any equivalent formulation of Equation (1) (e.g.,the entire calculation may be performed in the frequency domain), or inany other suitable way.

As described above, a primary-to-auxiliary transfer function may be usedto suppress noise in the MR data acquired by a primary RF coil in a MRIsystem such as a low-field MRI system. In some embodiments, theprimary-to-auxiliary transfer function may be estimated from calibrationmeasurements obtained by the primary RF coil and the auxiliary sensor.This may be done in any suitable way. For example, the PA transferfunction may be estimated from calibration measurements obtained when noMR signal is present or when the strength of the MR signal is smallrelative to the strength of the noise detected by the primary RF coil.As another example, the PA transfer function may be estimated fromcalibration measurements obtained when an MR signal is present (e.g.,during operation of the MRI system). Any suitable number of calibrationmeasurements may be used (e.g., at least 100, 100-1000, at least 1000,etc.). When more measurements are used, the PA transfer function may beestimated at a higher resolution (e.g., at more frequency values) and/orwith increased fidelity with respect to the actual noise environment.The PA transfer function may be estimated using a least-squaresestimation technique or any other suitable estimation technique, as thetechniques described herein are not limited to any particularcomputational method.

As one non-limiting example, when the signal acquired by the primarycoil at times {t_(k)} does not contain any MR signal or when thestrength of the MR signal is small relative to the strength of the noisedetected by the primary RF coil, then s_(pri)(t_(k))=n_(pri)(t_(k)), sothat the discrete Fourier transform of s_(pri)(t_(k)) is given by:

S _(pri)(ω_(k))=C _(pri)(ω_(k))U _(pri)(ω_(k)),

where C_(pri)(ω_(k)) is the discrete Fourier transform of C_(pri)(t_(k))and U_(pri)(ω_(k)) is the discrete Fourier transform of u_(pri)(t_(k)).Since C_(pri)(ω_(k))=H_(PA)(ω_(k)) S_(ref) (ω_(k)), the discrete Fouriertransform of the signal received at the primary coil may be representedas a function of the discrete Fourier transform of the signal receivedat the auxiliary sensor according to:

S _(pri)(ω_(k))=H _(PA)(ω_(k))S _(aux)(ω_(k))U _(pri)(ω_(k))  (2)

Equation (2) represents a set of independent equations, one for eachfrequency component, ω_(k). Since both U_(pri) and H_(PA) are unknown,it may not be possible to determine H_(PA) from a single calibrationmeasurement. If M calibration measurements (e.g., at least 10, at least100, at least 1000 calibration measurements) are made such that multipleexamples of S_(pri) and S_(aux) for each frequency component areobtained, then the PA transfer function can be determined despite theunknown U_(pri), via any suitable estimation technique, for example, vialeast squares estimation. This is so because multiple measurements maybe used to average out the uncorrelated noise. Given M calibrationmeasurements, a least squares estimator for the PA transfer function maybe obtained by considering the following matrix equation for eachfrequency component ω_(k),

${\begin{bmatrix}{S_{pri}( \omega_{k} )}_{1} \\\vdots \\{S_{pri}( \omega_{k} )}_{M}\end{bmatrix} = {{H_{PA}( \omega_{k} )}\begin{bmatrix}{S_{aux}( \omega_{k} )}_{1} \\\vdots \\{S_{aux}( \omega_{k} )}_{M}\end{bmatrix}}},$

which can be solved according to:

${H_{PA}( \omega_{k} )} = {{{\{ {\begin{bmatrix}{S_{aux}( \omega_{k} )}_{1} \\\vdots \\{S_{aux}( \omega_{k} )}_{M}\end{bmatrix}^{T}\begin{bmatrix}{S_{aux}( \omega_{k} )}_{1} \\\vdots \\{S_{aux}( \omega_{k} )}_{M}\end{bmatrix}} \}^{- 1}\begin{bmatrix}{S_{aux}( \omega_{k} )}_{1} \\\vdots \\{S_{aux}( \omega_{k} )}_{M}\end{bmatrix}}^{T}\begin{bmatrix}{S_{pri}( \omega_{k} )}_{1} \\\vdots \\{S_{pri}( \omega_{k} )}_{M}\end{bmatrix}}.}$

As may be appreciated from the foregoing, the above-described estimatoruses multiple measurements (i.e., M noise signals measured by each ofthe primary and auxiliary coils) to estimate the value of theprimary-to-auxiliary transfer function for multiple frequency bins. Thisresults in significantly improved estimates of the PA transfer functionas compared to techniques which rely on a single measurement (i.e., asingle signal measured by each of the primary and auxiliary coils) toestimate the transfer function. Such single-measurement techniques mayinclude scaling and time-shifting the reference signal beforesubtraction, which would correct for a difference in phase between thenoise signal as received at a primary coil and an auxiliary coil, but(unlike the multiple measurement technique described herein) would notcorrect for frequency-dependent phase differences.

Another single-measurement technique may include scaling and phaseadjusting the auxiliary noise signal in the frequency domain beforesubtracting it from the signal received at the primary coil. This couldbe accomplished by using the discrete Fourier transform (DFT) of thesignals received by a primary coil and an auxiliary coil. The optimalscaling and phase shift can be determined by a least-squares fit acrossmultiple frequency bins. For example, if S_(pri)(ω_(k)) is the DFT ofthe signal measured on the primary receive coil and S_(aux)(ω_(k)) isthe DFT of the signal measured on an auxiliary coil at the same time, anaverage scaling and phase shift SPF for a subset of frequency bins (inthe range of [k1,k2]) may be computed according to:

${SPF} = {\frac{\Sigma_{k\; 1}^{k\; 2}{S_{aux}( \omega_{k} )}{S_{pri}( \omega_{k} )}}{\Sigma_{k\; 1}^{k\; 2}{S_{aux}( \omega_{k} )}{S_{aux}( \omega_{k} )}}.}$

Although this single-measurement technique may be used to create afrequency-dependent correction, the method requires a tradeoff betweenfrequency resolution of the correction and accuracy of the estimation ofthe scaling and phase offset. In particular, this “averaging acrossfrequency bins of a single measurement” technique results in poor (e.g.,high-variance, biased) estimation of a PA transfer function. Incontrast, the above-described multiple measurement technique providesfor an unbiased and low-variance estimator.

As described above, the inventors have appreciated that the use ofmultiple coils may facilitate improved MRI in a number of ways,including more robust noise detection and/or cancellation, acceleratedimage acquisition, etc. In embodiments where multiple primary receivecoils and/or multiple auxiliary sensors are used, all of the sensors maybe the same type or may be of different types. For example, incircumstances where one or more RF coils are used as sensors, none,some, or all of the coils may be shielded. As another example, the coilscan have different sensitivities. When other types of sensors are used,at least some of the characteristics of the sensors and the primaryreceive coil(s) may necessarily be different, though some may be similaror the same.

In some embodiments, multiple auxiliary RF coils and/or primary RF coilsmay be used to accelerate imaging. For example, multiple RF coils usedto sense noise from the same or different noise sources may also be usedto perform parallel MR. In this manner, multiple RF coils may provideboth noise characterization functions as well as accelerated imageacquisition via their use as parallel receive coils.

In some embodiments, as described above, multiple sensors may be used toperform noise compensation in the presence of multiple noise sources. Inan environment having N correlated noise sources, where N is an integergreater than one, the Fourier transforms C_(pri)(ω) and C_(aux)(ω) ofnoise signals c_(pri)(t) and c_(aux)(t), received by a primary coil andan auxiliary sensor can be expressed as:

C _(pri)(ω)=H _(pri,1)(ω)C ₁(ω)+H _(pri,2)(ω)C ₂(ω)+ . . . +H_(pri,N)(ω)C _(N)(ω)

C _(aux)(ω)=H _(aux,1)(ω)C ₁(ω)+H _(aux,2)(ω)C ₂(ω)+ . . . +H_(aux,N)(ω)C _(N)(ω),

where C_(j)(ω); 1≤j≤N, is a Fourier transform of a noise signal from thejth noise source, H_(pri,j) (ω) is a transfer function between theprimary coil and the jth noise source, and H_(aux,j) (ω) is a transferfunction between the auxiliary sensor and the jth noise source. When theratio H_(pri,j)(ω)/H_(aux,j)(ω) is different for one or more noisesources, it may not be possible to perform high quality noisecompensation by using only a single auxiliary sensor. However, multipleauxiliary sensors may be used to perform noise compensation in thiscircumstance as described below.

Described below is a non-limiting example of how multiple auxiliarysensors may be used to perform noise compensation for multiple differentnoise sources. Without loss of generality, suppose a MR system has aprimary coil and P auxiliary sensors (where P is any integer greaterthan or equal to 1). Further, suppose that the MR system is deployed inan environment in which there are N different noise sources (where N isan integer greater than or equal to 1). Let H_(ij)(ω) denote thetransfer function between the ith auxiliary sensor (where 1≤i≤P) and thejth noise source (where 1≤j≤N). The following set of equations relatethe Fourier transforms of the signals received by the auxiliary sensorsto the Fourier transforms of the noise signals produced by the noisesources:

${{\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}\begin{bmatrix}C_{1} \\\vdots \\C_{N}\end{bmatrix}} = \begin{bmatrix}C_{{aux},1} \\\vdots \\C_{{aux},P}\end{bmatrix}},$

where C_(aux,i); 1≤i≤P, is a Fourier transform of the signal received atthe ith auxiliary sensor, C_(j)(ω); 1≤j≤N is a Fourier transform of anoise signal from the jth noise source, and where the dependence of allthe terms on frequency is not shown explicitly (the (ω) is suppressedfor brevity), though it should be appreciated that all the terms in theabove matrix equation are functions of frequency.

When the number of auxiliary sensors is greater than or equal to thenumber of noise sources (i.e., P>=N), the above matrix equation may besolved for the noise signals according to:

$\begin{bmatrix}C_{1} \\\vdots \\C_{N}\end{bmatrix} = {{{\{ {\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}^{T}\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}} \}^{- 1}\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}}^{T}\begin{bmatrix}C_{{aux},1} \\\vdots \\C_{{aux},P}\end{bmatrix}}.}$

If such a solution exists, the correlated noise measured on the primaryreceive coil may be expressed in relation to the measurements obtainedby all of the auxiliary sensors according to:

$C_{pri} = {\lbrack {H_{{pri},1}\mspace{14mu} \cdots \mspace{14mu} H_{{pri},N}} \rbrack {{\{ {\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}^{T}\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}} \}^{- 1}\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}}^{T}\begin{bmatrix}C_{{aux},1} \\\vdots \\C_{{aux},P}\end{bmatrix}}}$

A multi-channel transfer function H_(MPA) may be defined according to:

$H_{MPA} = {\lbrack {H_{{PA},1}\mspace{14mu} \cdots \mspace{14mu} H_{{PA},P}} \rbrack = {\lbrack {H_{{pri},1}\mspace{14mu} \cdots \mspace{14mu} H_{{pri},N}} \rbrack {{\{ {\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}^{T}\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}} \}^{- 1}\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}}^{T}.}}}$

It may then be seen that the noise measured by the primary receive coilis a linear combination of the noise signals measured on all theauxiliary coils:

$\begin{matrix}{C_{pri} = {{\lbrack {H_{{PA},1}\mspace{14mu} \cdots \mspace{14mu} H_{{PA},P}} \rbrack \begin{bmatrix}C_{{aux},1} \\\vdots \\C_{{aux},P}\end{bmatrix}}.}} & (3)\end{matrix}$

Thus, given noise signals measured by P auxiliary sensors (e.g., theFourier transforms of which are given by C_(aux,i) for 1≤i≤P), the aboveequation may be used to estimate the noise signal received at theprimary receive coil (e.g., the Fourier transform of which is given byC_(pri)). In turn, the estimated noise signal may be subtracted from theoverall signal measured by the primary receive coil (which signal wouldhave both an MR signal component and a noise component) to perform noisesuppression.

However, to use the above equation (3), an estimate of the multichannelprimary-to-auxiliary transfer function H_(MPA)=[H_(PARC,1) . . .H_(PARC,P)] is needed. This may be achieved in any suitable way and, insome embodiments, may be done by making multiple measurements using theprimary receive coil and the auxiliary sensors (e.g., at a time whenthere is no MR signal present) and using these measurements to estimatethe multichannel primary-to-auxiliary transfer function. For example,given M measurements of noise signals at each of the P auxiliary sensorsand the primary receive coil, the H_(MPA) may be estimated for eachfrequency component ω_(k) (where k is an index over frequency bins)using least squares estimation according to:

${\begin{bmatrix}{H_{{PA},1}( \omega_{k} )} \\\vdots \\{H_{{PA},P}( \omega_{k} )}\end{bmatrix} = {\{ {\begin{bmatrix}{S_{{aux},1}( \omega_{k} )}_{1} & \cdots & {S_{{aux},P}( \omega_{k} )}_{1} \\\vdots & \ddots & \vdots \\{S_{{aux},1}( \omega_{k} )}_{M} & \cdots & {S_{{aux},P}( \omega_{k} )}_{M}\end{bmatrix}^{T}\begin{bmatrix}{S_{{aux},1}( \omega_{k} )}_{1} & \cdots & {S_{{aux},P}( \omega_{k} )}_{1} \\\vdots & \ddots & \vdots \\{S_{{aux},1}( \omega_{k} )}_{M} & \cdots & {S_{{aux},P}( \omega_{k} )}_{M}\end{bmatrix}} \}^{- 1} \times \begin{bmatrix}{S_{{aux},1}( \omega_{k} )}_{1} & \cdots & {S_{{aux},P}( \omega_{k} )}_{1} \\\vdots & \ddots & \vdots \\{S_{{aux},1}( \omega_{k} )}_{M} & \cdots & {S_{{aux},P}( \omega_{k} )}_{M}\end{bmatrix} \times \begin{bmatrix}{S_{pri}( \omega_{k} )}_{1} \\\vdots \\{S_{pri}( \omega_{k} )}_{M}\end{bmatrix}}},$

where S_(aux,i)(ω_(k))_(m) represents the value of the kth frequency binof the Fourier transform of the mth measured signal obtained by the ithauxiliary sensor, and where S_(pri)(ω_(k))_(m) represents the value ofthe kth frequency bin of the Fourier transform of the mth measuredsignal obtained by the primary receive coil. This least-squares approachprovides the most complete correction when the columns of the followingmatrix are as orthogonal as possible to one another:

$\begin{bmatrix}H_{11} & \cdots & H_{1N} \\\vdots & \ddots & \vdots \\H_{P\; 1} & \cdots & H_{PN}\end{bmatrix}.$

Put another way, each auxiliary sensor may detect some or all of thedifferent noise sources in a unique way from other auxiliary sensors. Inorder to correct for the presence of near field sources, multiplesensors may be placed in different locations to be more or lesssensitive to some of the noise sources. In some embodiments, multiplesensors may be oriented orthogonally to one another (e.g., one sensormay be oriented in an “X” direction, another sensor may be oriented inthe “Y” direction, and another sensor may be oriented in a “Z”direction). In this way, each vector of the time varying interferencefields may be captured. It may also be beneficial to use one or moreantennas as an auxiliary sensor to provide another orthogonalmeasurement.

It should be appreciated that the techniques described herein facilitatedetecting noise in the environment of an MRI system using any numberand/or type of sensor suitable for detecting noise produced byrespective noise sources. As a result, noise from a variety of sourcesthat may impact the performance of the MRI system may be detected andused to suppress and/or eliminate noise from MR signals detected by theMRI system during operation. Because techniques described herein operateon the particular noise environment of the MRI system, the noisesuppression techniques described herein facilitate deployment of an MRIsystem wherever the system may be needed, eliminating the requirementthat the system be installed in specially shielded rooms. The ability todynamically adapt to changing noise environments facilitates developmentof MRI systems that can be deployed in generally noisy environments,including environments where noise sources may change over time. Becausetechniques described herein can be utilized during operation of the MRIsystem, the noise environment can be characterized dynamically so thatit reflects the same noise environment to which the system is currentlybeing exposed. When utilized in connection with a low-field MRI system,a cost effective, high availability and transportable MRI solution maybe achieved in part using the noise suppression techniques describedherein.

Having thus described several aspects and embodiments of the technologyset forth in the disclosure, it is to be appreciated that variousalterations, modifications, and improvements will readily occur to thoseskilled in the art. Such alterations, modifications, and improvementsare intended to be within the spirit and scope of the technologydescribed herein. For example, those of ordinary skill in the art willreadily envision a variety of other means and/or structures forperforming the function and/or obtaining the results and/or one or moreof the advantages described herein, and each of such variations and/ormodifications is deemed to be within the scope of the embodimentsdescribed herein. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific embodiments described herein. It is, therefore, to beunderstood that the foregoing embodiments are presented by way ofexample only and that, within the scope of the appended claims andequivalents thereto, inventive embodiments may be practiced otherwisethan as specifically described. In addition, any combination of two ormore features, systems, articles, materials, kits, and/or methodsdescribed herein, if such features, systems, articles, materials, kits,and/or methods are not mutually inconsistent, is included within thescope of the present disclosure.

The above-described embodiments can be implemented in any of numerousways. One or more aspects and embodiments of the present disclosureinvolving the performance of processes or methods may utilize programinstructions executable by a device (e.g., a computer, a processor, orother device) to perform, or control performance of, the processes ormethods. In this respect, various inventive concepts may be embodied asa computer readable storage medium (or multiple computer readablestorage media) (e.g., a computer memory, one or more floppy discs,compact discs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement one or more of the variousembodiments described above. The computer readable medium or media canbe transportable, such that the program or programs stored thereon canbe loaded onto one or more different computers or other processors toimplement various ones of the aspects described above. In someembodiments, computer readable media may be non-transitory media.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects as described above. Additionally,it should be appreciated that according to one aspect, one or morecomputer programs that when executed perform methods of the presentdisclosure need not reside on a single computer or processor, but may bedistributed in a modular fashion among a number of different computersor processors to implement various aspects of the present disclosure.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

The above-described embodiments of the present invention can beimplemented in any of numerous ways. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. It should beappreciated that any component or collection of components that performthe functions described above can be generically considered as acontroller that controls the above-discussed function. A controller canbe implemented in numerous ways, such as with dedicated hardware, orwith general purpose hardware (e.g., one or more processor) that isprogrammed using microcode or software to perform the functions recitedabove, and may be implemented in a combination of ways when thecontroller corresponds to multiple components of a system.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer, as non-limitingexamples. Additionally, a computer may be embedded in a device notgenerally regarded as a computer but with suitable processingcapabilities, including a Personal Digital Assistant (PDA), a smartphoneor any other suitable portable or fixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audibleformats.

Such computers may be interconnected by one or more networks in anysuitable form, including a local area network or a wide area network,such as an enterprise network, and intelligent network (IN) or theInternet. Such networks may be based on any suitable technology and mayoperate according to any suitable protocol and may include wirelessnetworks, wired networks or fiber optic networks.

Also, as described, some aspects may be embodied as one or more methods.The acts performed as part of the method may be ordered in any suitableway. Accordingly, embodiments may be constructed in which acts areperformed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively.

What is claimed is:
 1. A system for suppressing noise detected in anenvironment of a magnetic resonance imaging system, the systemcomprising: at least one controller configured to perform: acquiring aplurality of magnetic resonance signals by repeatedly applying a firstpulse sequence using a first spatial encoding; and estimating noisebased, at least in part, on a comparison of the plurality of magneticresonance signals.